(532f) Life Cycle Optimization on Sustainable Design of Cellulosic Biopower Supply Chains
With a large fleet of aging power plants and increasingly stringent environmental regulations and policies, the country is exploiting its energy portfolio and seeking potential sustainable alternatives for its electricity supply. As one of the most promising renewable power generation options, biopower from cellulosic biomass offers the advantage of being dispatchable and having minimum implications on food market, considerable potentials to waste reduction, and abundant domestic availability across the country [1, 2]. The major obstacle at the current stage for large-scale implementation of biopower supply systems lies in the development of an efficient supply chain network that links the biomass supply with biopower conversion facilities [3-5]. Another perspective to note is that, although cellulosic biomass resources are considered carbon-neutral, activities such as biomass collection, transportation, storage and material processing would incur positive greenhouse gas (GHG) emissions, which can have implications on global warming and climate change [6, 7]. Hence, it is essential to simultaneously consider the economic performance as well as the environmental impacts when addressing the design and operation of bio-electricity supply chains.
It is the goal of this work to develop a generic optimization model for sustainable design and operation of biopower supply chains, which can simultaneously evaluate and optimize the economic and environmental performances by achieving the optimal decisions in network design, technology selection, facility location and sizing, transportation and storage, etc. [8, 9]. To address this challenge, we propose a multi-objective, multi-period mixed-integer linear fractional programming (MILFP) model that captures the critical features of the biopower supply chain, including geographically disperse distribution of biomass, seasonality in biomass supply, water content and degradation issues, etc. . Levelized cost of electricity is chosen as the economic metrics, which is extensively used as the major indicator for the projected economic performance of a renewable energy system. The environmental impact associated with the production of a unit amount of bioelectricity is chosen as the environmental metrics, which is evaluated based on a cradle-to-gate life cycle assessment and would lead to a more environmentally sustainable way of manufacturing [11-14]. We note that MILFP is a special class of mixed-integer nonlinear program (MINLP). Due to the non-convexity and combinatorial nature of the resulting MILFP problems, it can be challenging for the global optimization of large-scale supply chain problems. To tackle this issue, we further present two tailored MILFP solution approaches, namely the parametric algorithm [15, 16] and the reformulation-linearization method . To illustrate the application, we present a county-level case study on a potential biopower supply chain in the state of Illinois. The resulting Pareto curve reveals how the inherent tradeoffs between the economic and environmental objectives influence the design and operational decisions. On the other hand, it is shown that the tailored solution methods excel general-purpose MINLP methods both in terms of solution time and quality.
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